Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

102
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
102
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

479
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
479
EPS and iPS Cells in Disease Research01:21

EPS and iPS Cells in Disease Research

2.8K
Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
2.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A decision-theoretic framework for uncertainty quantification in epidemiological modelling.

American journal of epidemiology·2026
Same author

Patterns of emergence and circulation of West Nile Virus in Algeria.

Nature communications·2026
Same author

Integrated surveillance resolves Darién paradox of Oropouche virus emergence in Panama's migration corridor.

Research square·2026
Same author

Empowering One Health with metagenomics.

One health outlook·2026
Same author

Clinical Characteristics of Patients Infected with Bundibugyo Virus, DRC 2026.

The New England journal of medicine·2026
Same author

Real-time epidemic intelligence in a public health emergency: the 2026 Bundibugyo virus outbreak.

The Lancet. Infectious diseases·2026

Related Experiment Video

Updated: May 27, 2025

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.1K

Artificial intelligence for modelling infectious disease epidemics.

Moritz U G Kraemer1,2, Joseph L-H Tsui3,4, Serina Y Chang5,6

  • 1Pandemic Sciences Institute, University of Oxford, Oxford, UK. moritz.kraemer@biology.ox.ac.uk.

Nature
|February 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can enhance infectious disease epidemiology by accelerating research and improving surveillance. This technology offers powerful tools for understanding and combating public health threats.

More Related Videos

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

Related Experiment Videos

Last Updated: May 27, 2025

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.1K
A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Artificial Intelligence

Background:

  • Infectious disease threats are diverse and unpredictable.
  • Artificial intelligence (AI) is increasingly used in decision-making across various fields.
  • AI has the potential to significantly advance infectious disease epidemiology.

Purpose of the Study:

  • To explore the application of AI in infectious disease modeling.
  • To discuss how AI can address key epidemiological questions.
  • To examine the social context and limitations of AI in this domain.

Main Methods:

  • Review of AI systems combining machine learning, computational statistics, information retrieval, and data science.
  • Application of AI methods to infectious disease surveillance data.
  • Analysis of social aspects including explainability, safety, accountability, and ethics.

Main Results:

  • AI can accelerate breakthroughs in epidemiological research.
  • Specific AI methods can be applied to routinely collected surveillance data.
  • The social context of AI implementation requires careful consideration.

Conclusions:

  • AI offers transformative potential for infectious disease epidemiology.
  • Effective harnessing of AI requires addressing ethical and practical challenges.
  • Recommendations are provided for maximizing AI's impact on public health.